A stochastic view on surface inhomogeneity of nanoparticles

Point-Spread Function Deformations Unlock 3D Localization Microscopy on Spherical Nanoparticles

Nanoparticles (NPs) have proven their applicability in biosensing, drug delivery, and photothermal therapy, but their performance depends critically on the distribution and number of functional groups on their surface. When studying surface functionalization using super-resolution microscopy, the NP modifies the fluorophore's point-spread function (PSF). This leads to systematic mislocalizations in conventional analyses employing Gaussian PSFs. Here, we address this shortcoming by deriving the analytical PSF model for a fluorophore near a spherical NP. Its calculation is four orders of magnitude faster than numerical approaches and thus feasible for direct use in localization algorithms. We fit this model to individual 2D images from DNA-PAINT experiments on DNA-coated gold NPs and demonstrate extraction of the 3D positions of functional groups with <5 nm precision, revealing inhomogeneous surface coverage. Our method is exact, fast, accessible, and poised to become the standard in super-resolution imaging of NPs for biosensing and drug delivery applications.

Nanotherapeutic Heterogeneity: Sources, Effects, and Solutions

Nanomaterials have revolutionized medicine by enabling control over drugs' pharmacokinetics, biodistribution, and biocompatibility. However, most nanotherapeutic batches are highly heterogeneous, meaning they comprise nanoparticles that vary in size, shape, charge, composition, and ligand functionalization. Similarly, individual nanotherapeutics often have heterogeneously distributed components, ligands, and charges. This review discusses nanotherapeutic heterogeneity's sources and effects on experimental readouts and therapeutic efficacy. Among other topics, it demonstrates that heterogeneity exists in nearly all nanotherapeutic types, examines how nanotherapeutic heterogeneity arises, and discusses how heterogeneity impacts nanomaterials' in vitro and in vivo behavior. How nanotherapeutic heterogeneity skews experimental readouts and complicates their optimization and clinical translation is also shown. Lastly, strategies for limiting nanotherapeutic heterogeneity are reviewed and recommendations for developing more reproducible and effective nanotherapeutics provided.

Unveiling protein corona formation around self-propelled enzyme nanomotors by nanoscopy

The interaction of nanoparticles with biological media is a topic of general interest for drug delivery systems and among those for active nanoparticles, also called nanomotors. Herein, we report the use of super resolution microscopy, in particular, stochastic optical reconstruction microscopy (STORM), to characterize the formation of a protein corona around active enzyme-powered nanomotors. First, we characterized the distribution and number of enzymes on nano-sized particles and characterized their motion capabilities. Then, we incubated the nanomotors with fluorescently labelled serum proteins. Interestingly, we observed a significant decrease of protein corona formation (20%) and different composition, which was studied by proteomic analysis. Moreover, motion was not hindered, as nanomotors displayed enhanced diffusion regardless of the protein corona. Elucidating how active particles interact with biological media and maintain their self-propulsion after protein corona formation will pave the way for the use of these systems in complex biological fluids in biomedicine.

Assembly Characterization of Human Equilibrium Nucleoside Transporter 1 (hENT1) by Inhibitor Probe-Based dSTORM Imaging

Nucleoside transporters (NTs) play an important role in the metabolism of nucleoside substances and the efficacy of nucleoside drugs. Its spatial information related to biofunctions at the single-molecule level remains unclear, owing to the limitation of the existing labeling methods and traditional imaging methods. Therefore, we synthesize the inhibitor-based fluorescent probe SAENTA-Cy5 and apply direct stochastic optical reconstruction microscopy (dSTORM) to conduct refined observation of human equilibrative nucleoside transporter 1 (hENT1), the most important and famous member of NTs. We first demonstrate the labeling specificity and superiority of SAENTA-Cy5 to the antibody probe. Then, we found different assembly patterns of hENT1 on the apical and basal membranes, which are further investigated to be caused by varying associations of membrane carbohydrates, membrane classical functional domains (lipid rafts), and associated membrane proteins (EpCAM). Our work provides an efficient method for labeling hENT1, which contributes to realize fine observation of NTs. The findings on the assembly features and potential assembly mechanism of hENT1 promote a better understanding of its biofunction, which facilitates further investigations on how NTs work in the metabolism of nucleoside and nucleoside analogues.

Mechanistic Understanding of Protein Corona Formation around Nanoparticles: Old Puzzles and New Insights

Although a wide variety of nanoparticles (NPs) have been engineered for use as disease markers or drug delivery agents, the number of nanomedicines in clinical use has hitherto remained small. A key obstacle in nanomedicine development is the lack of a deep mechanistic understanding of NP interactions in the bio-environment. Here, the focus is on the biomolecular adsorption layer (protein corona), which quickly enshrouds a pristine NP exposed to a biofluid and modifies the way the NP interacts with the bio-environment. After a brief introduction of NPs for nanomedicine, proteins, and their mutual interactions, research aimed at addressing fundamental properties of the protein corona, specifically its mono-/multilayer structure, reversibility and irreversibility, time dependence, as well as its role in NP agglomeration, is critically reviewed. It becomes quite evident that the knowledge of the protein corona is still fragmented, and conflicting results on fundamental issues call for further mechanistic studies. The article concludes with a discussion of future research directions that should be taken to advance the understanding of the protein corona around NPs. This knowledge will provide NP developers with the predictive power to account for these interactions in the design of efficacious nanomedicines.

Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles

The reshuffling mobility of molecular building blocks in self-assembled micelles is a key determinant of many their interesting properties, from emerging morphologies and surface compartmentalization, to dynamic reconfigurability and stimuli-responsiveness. However, the microscopic details of such complex structural dynamics are typically nontrivial to elucidate, especially in multicomponent assemblies. Here we show a machine-learning approach that allows us to reconstruct the structural and dynamic complexity of mono- and bicomponent surfactant micelles from high-dimensional data extracted from equilibrium molecular dynamics simulations. Unsupervised clustering of smooth overlap of atomic position (SOAP) data enables us to identify, in a set of multicomponent surfactant micelles, the dominant local molecular environments that emerge within them and to retrace their dynamics, in terms of exchange probabilities and transition pathways of the constituent building blocks. Tested on a variety of micelles differing in size and in the chemical nature of the constitutive self-assembling units, this approach effectively recognizes the molecular motifs populating them in an exquisitely agnostic and unsupervised way, and allows correlating them to their composition in terms of constitutive surfactant species.

Multicolor Super-Resolution Microscopy of Protein Corona on Single Nanoparticles

Nanoparticles represent a promising class of material for nanomedicine and molecular biosensing. The formation of a protein corona due to nonspecific particle-protein interactions is a determining factor for the biological fate of nanoparticles in vivo and strongly impacts the performance of nanoparticles when used as biosensors. Nonspecific interactions are usually highly heterogeneous, yet little is known about the heterogeneity of the protein corona that may lead to inter- and intraparticle differences in composition and protein distribution. Here, we present a super-resolution microscopic approach to study the protein corona on single silica nanoparticles and subsequent cellular interactions using multicolor stimulated emission depletion (STED) microscopy. We demonstrate that STED resolves structural features of protein corona on single particles including the distribution on the particle surface and the degree of protein internalization in porous particles. Using multicolor measurements of multiple labeled protein species, we determine the composition of the protein corona at the single-particle level. We quantify particle-to-particle differences in the composition and find that the composition is considerably influenced by the particle geometry. In a subsequent cellular uptake measurement, we demonstrate multicolor STED of protein corona on single particles internalized by cells. Our study shows that STED microscopy opens the window toward mechanistic understanding of protein coronas and aids in the rational design of nanoparticles as nanomedicines and biosensors.

A Single-Molecule View at Nanoparticle Targeting Selectivity: Correlating Ligand Functionality and Cell Receptor Density

Antibody-functionalized nanoparticles (NPs) are commonly used to increase the targeting selectivity toward cells of interest. At a molecular level, the number of functional antibodies on the NP surface and the density of receptors on the target cell determine the targeting interaction. To rationally develop selective NPs, the single-molecule quantitation of both parameters is highly desirable. However, techniques able to count molecules with a nanometric resolution are scarce. Here, we developed a labeling approach to quantify the number of functional cetuximabs conjugated to NPs and the expression of epidermal growth factor receptors (EGFRs) in breast cancer cells using direct stochastic optical reconstruction microscopy (dSTORM). The single-molecule resolution of dSTORM allows quantifying molecules at the nanoscale, giving a detailed insight into the distributions of individual NP ligands and cell receptors. Additionally, we predicted the fraction of accessible antibody-conjugated NPs using a geometrical model, showing that the total number exceeds the accessible number of antibodies. Finally, we correlated the NP functionality, cell receptor density, and NP uptake to identify the highest cell uptake selectivity regimes. We conclude that single-molecule functionality mapping using dSTORM provides a molecular understanding of NP targeting, aiding the rational design of selective nanomedicines.

Quantifying the effect of PEG architecture on nanoparticle ligand availability using DNA-PAINT

The importance of PEG architecture on nanoparticle (NP) functionality is known but still difficult to investigate, especially at a single particle level. Here, we apply DNA Point Accumulation for Imaging in Nanoscale Topography (DNA-PAINT), a super-resolution microscopy (SRM) technique, to study the surface functionality in poly(lactide-co-glycolide)-poly(ethylene glycol) (PLGA-PEG) NPs with different PEG structures. We demonstrated how the length of the PEG spacer can influence the accessibility of surface chemical functionality, highlighting the importance of SRM techniques to support the rational design of functionalized NPs.

Correlating Super-Resolution Microscopy and Transmission Electron Microscopy Reveals Multiparametric Heterogeneity in Nanoparticles

The functionalization of nanoparticles with functional moieties is a key strategy to achieve cell targeting in nanomedicine. The interplay between size and ligand number is crucial for the formulation performance and needs to be properly characterized to understand nanoparticle structure-activity relations. However, there is a lack of methods able to measure both size and ligand number at the same time and at the single particle level. Here, we address this issue by introducing a correlative light and electron microscopy (CLEM) method combining super-resolution microscopy (SRM) and transmission electron microscopy (TEM) imaging. We apply our super-resCLEM method to characterize the relationship between size and ligand number and density in PLGA-PEG nanoparticles. We highlight how heterogeneity found in size can impact ligand distribution and how a significant part of the nanoparticle population goes completely undetected in the single-technique analysis. Super-resCLEM holds great promise for the multiparametric analysis of other parameters and nanomaterials.

Advanced Static and Dynamic Fluorescence Microscopy Techniques to Investigate Drug Delivery Systems

In the past decade(s), fluorescence microscopy and laser scanning confocal microscopy (LSCM) have been widely employed to investigate biological and biomimetic systems for pharmaceutical applications, to determine the localization of drugs in tissues or entire organisms or the extent of their cellular uptake (in vitro). However, the diffraction limit of light, which limits the resolution to hundreds of nanometers, has for long time restricted the extent and quality of information and insight achievable through these techniques. The advent of super-resolution microscopic techniques, recognized with the 2014 Nobel prize in Chemistry, revolutionized the field thanks to the possibility to achieve nanometric resolution, i.e., the typical scale length of chemical and biological phenomena. Since then, fluorescence microscopy-related techniques have acquired renewed interest for the scientific community, both from the perspective of instrument/techniques development and from the perspective of the advanced scientific applications. In this contribution we will review the application of these techniques to the field of drug delivery, discussing how the latest advancements of static and dynamic methodologies have tremendously expanded the experimental opportunities for the characterization of drug delivery systems and for the understanding of their behaviour in biologically relevant environments.

A quantitative view on multivalent nanomedicine targeting

Although the concept of selective delivery has been postulated over 100 years ago, no targeted nanomedicine has been clinically approved so far. Nanoparticles modified with targeting ligands to promote the selective delivery of therapeutics towards a specific cell population have been extensively reported. However, the rational design of selective particles is still challenging. One of the main reasons for this is the lack of quantitative theoretical and experimental understanding of the interactions involved in cell targeting. In this review, we discuss new theoretical models and experimental methods that provide a quantitative view of targeting. We show the new advancements in multivalency theory enabling the rational design of super-selective nanoparticles. Furthermore, we present the innovative approaches to obtain key targeting parameters at the single-cell and single molecule level and their role in the design of targeting nanoparticles. We believe that the combination of new theoretical multivalent design and experimental methods to quantify receptors and ligands aids in the rational design and clinical translation of targeted nanomedicines.

Nano-targeting lessons from the SARS-CoV-2

The lack of targeting efficacy has frequently led functionalized nanoparticles to accumulate in unwanted cells and tissues while boosting toxicity-related effects. Conversely, viruses are natural nanoparticles that precisely and responsively interact with the biological machinery through an effective-driven fashion. This interaction is enhanced by a meticulous spatial arrangement which results in a quasi-crystalline distribution of proteins on the viruses' surface. Amidst the COVID-19 pandemic, we propose to look at the SARS-CoV-2 nanoscale viral scaffold as an example of a highly-ordered architecture that must inspire and tailor the production of targeted synthetic nanoparticles.

PAINT-ing Fluorenylmethoxycarbonyl (Fmoc)-Diphenylalanine Hydrogels

Self-assembly of fluorenylmethoxycarbonyl-protected diphenylalanine (FmocFF) in water is widely known to produce hydrogels. Typically, confocal microscopy is used to visualize such hydrogels under wet conditions, that is, without freezing or drying. However, key aspects of hydrogels like fiber diameter, network morphology and mesh size are sub-diffraction limited features and cannot be visualized effectively using this approach. In this work, we show that it is possible to image FmocFF hydrogels by Points Accumulation for Imaging in Nanoscale Topography (PAINT) in native conditions and without direct gel labelling. We demonstrate that the fiber network can be visualized with improved resolution (≈50 nm) both in 2D and 3D. Quantitative information is extracted such as mesh size and fiber diameter. This method can complement the existing characterization tools for hydrogels and provide useful information supporting the design of new materials.

Super-resolution Microscopy for Nanomedicine Research

Super-resolution microscopy, or nanoscopy, revolutionized the field of cell biology, enabling researchers to visualize cellular structures with nanometric resolution, single-molecule sensitivity, and in multiple colors. However, the impact of these techniques goes beyond biology as the fields of nanotechnology and nanomedicine can greatly benefit from them, as well. Nanoscopy can visualize nanostructures in vitro and in cells and can contribute to the characterization of their structures and nano-bio interactions. In this Perspective, we discuss the potential of super-resolution imaging for nanomedicine research, its technical challenges, and the future developments we envision for this technology.

Quantification of trans-synaptic protein alignment: A data analysis case for single-molecule localization microscopy

Nanoscale distribution of proteins and their relative positioning within a defined subcellular region are key to their physiological functions. Thanks to the super-resolution imaging methods, especially single-molecule localization microscopy (SMLM), mapping the three-dimensional distribution of multiple proteins has been easier and more efficient than ever. Nevertheless, in spite of the many tools available for efficient localization detection and image rendering, it has been a challenge to quantitatively analyze the 3D distribution and relative positioning of proteins in these SMLM data. Here, using heterogeneously distributed synaptic proteins as examples, we describe in detail a series of analytical methods including detection of nanoscale density clusters, quantification of the trans-synaptic alignment between these protein densities, and automatic en face projection and averaging. These analyses were performed within customized Matlab routines and we make the full scripts available. The concepts behind these analytical methods and the scripts can be adapted for quantitative analysis of spatial organization of other macromolecular complexes.